The ever-growing amount of data available on the Internet calls for personalization. Yet, the most effective personalization schemes, such as those based on collaborative filtering (CF), are notoriously resource greedy. We argue that scalable infrastructures should rely on P2P design to scale to that increasing number of users, data and dynamics.
In this talk, I will present a scalable algorithm for collaborative filtering, which P2P flavor provides scalability by design. This scheme can been instanciated in various settings: P2P, hybrid infrastructure offloading CPU-intensive recommendation tasks to front-end client browsers, while retaining storage and orchestration tasks within back-end servers, as well as centralized infrastructures. As personalization relies on users giving away information, I will also present the potential encountered privacy issues and a range of solutions to preserve users privacy in such systems.
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